{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "DataAnalysis"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tools import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "source": [
    "# Chap2 获取文本语料库 和 词汇资源\n",
    "\n",
    "目的：\n",
    "\n",
    "1.  什么是有用的文本语料库和词汇资源？如何使用 Python 获取它们？\n",
    "2.  哪些 Python 结构最适合这项工作？\n",
    "3.  编写 Python 代码时如何避免重复的工作？"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## 2.2 条件频率分布\n",
    "是频率分布的集合，每个频率分布有一个不同的“条件”。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 2.2.1 条件 和 事件\n",
    "\n",
    "-   频率分布计算观察到的事件。\n",
    "-   条件频率分布需要给每个事件关联一个条件，所以处理的是一系列配对序列\n",
    "\n",
    "配对序列中每对的形式是：(condition, 事件 ) 根据 condition（条件）统计 event (事件) 的频率。"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "text=  ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', Ellipsis]\npairs=  [('news', 'The'), ('news', 'Fulton'), ('news', 'County'), Ellipsis]\n"
     ]
    }
   ],
   "source": [
    "text=['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]\n",
    "pairs=[('news', 'The'), ('news', 'Fulton'), ('news', 'County'),...]\n",
    "print(\"text= \", text)\n",
    "print(\"pairs= \", pairs)"
   ]
  },
  {
   "source": [
    "### 2.2.2 按文体计数词汇"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "            the    cute  Monday   could    will \n   news    5580       0       0      86     389 \nromance    2758       2       0     193      43 \n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import brown\n",
    "\n",
    "cfd=nltk.ConditionalFreqDist(\n",
    "    (genre, word)\n",
    "    for genre in ['news', 'romance']\n",
    "    for word in brown.words(categories=genre)\n",
    ")\n",
    "cfd.tabulate(samples=['the', 'cute', ' Monday', 'could', 'will'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "genre word count=  170576\ngenre_word[:4]=  [('news', 'The'), ('news', 'Fulton'), ('news', 'County'), ('news', 'Grand')]\ngenre_word[-4:]=  [('romance', 'afraid'), ('romance', 'not'), ('romance', \"''\"), ('romance', '.')]\n"
     ]
    }
   ],
   "source": [
    "genre_word = [\n",
    "    (genre, word)\n",
    "    for genre in ['news', 'romance']\n",
    "    for word in brown.words(categories=genre)\n",
    "]\n",
    "print(\"genre word count= \", len(genre_word))\n",
    "print(\"genre_word[:4]= \",genre_word[:4])\n",
    "print(\"genre_word[-4:]= \",genre_word[-4:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cfd.conditions()=  ['news', 'romance']\n"
     ]
    }
   ],
   "source": [
    "cfd = nltk.ConditionalFreqDist(genre_word)\n",
    "print(\"cfd.conditions()= \", cfd.conditions())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >cfd['news']< ---------------\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "FreqDist({'the': 5580, ',': 5188, '.': 4030, 'of': 2849, 'and': 2146, 'to': 2116, 'a': 1993, 'in': 1893, 'for': 943, 'The': 806, ...})"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "show_subtitle(\"cfd['news']\")\n",
    "cfd['news']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >cfd['romance']< ---------------\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "FreqDist({',': 3899, '.': 3736, 'the': 2758, 'and': 1776, 'to': 1502, 'a': 1335, 'of': 1186, '``': 1045, \"''\": 1044, 'was': 993, ...})"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "show_subtitle(\"cfd['romance']\")\n",
    "cfd['romance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cfd['romance'].most_common(20= [(',', 3899), ('.', 3736), ('the', 2758), ('and', 1776), ('to', 1502), ('a', 1335), ('of', 1186), ('``', 1045), (\"''\", 1044), ('was', 993), ('I', 951), ('in', 875), ('he', 702), ('had', 692), ('?', 690), ('her', 651), ('that', 583), ('it', 573), ('his', 559), ('she', 496)]\n"
     ]
    }
   ],
   "source": [
    "print(\"cfd['romance'].most_common(20=\", cfd['romance'].most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cfd['romance']['could']=  193\n"
     ]
    }
   ],
   "source": [
    "print(\"cfd['romance']['could']= \", cfd['romance']['could'])"
   ]
  },
  {
   "source": [
    "### 2.2.3 绘制分布图 显示分布表"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "        1789 1793 1797 \namerica    2    1    8 \ncitizen    5    1    6 \n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import inaugural\n",
    "\n",
    "cfd=nltk.ConditionalFreqDist(\n",
    "    (target, fileid[:4])\n",
    "    for fileid in inaugural.fileids()\n",
    "    for w in inaugural.words(fileid)\n",
    "    for target in ['america', 'citizen']\n",
    "    if w.lower().startswith(target)\n",
    ")\n",
    "cfd.plot()\n",
    "cfd.tabulate(samples=['1789', '1793', '1797'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                  1    2    3    4    5    6    7    8    9 \n       English  185  525  883  997 1166 1283 1440 1558 1638 \nGerman_Deutsch  171  263  614  717  894 1013 1110 1213 1275 \n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import udhr\n",
    "\n",
    "languages=['Chickasaw', 'English', 'German_Deutsch', 'Greenlandic_Inuktikut', 'Hungarian_Magyar', 'Ibibio_Efik']\n",
    "cfd=nltk.ConditionalFreqDist(\n",
    "    (lang, len(word))\n",
    "    for lang in languages\n",
    "    for word in udhr.words(lang+'-Latin1')\n",
    ")\n",
    "cfd.plot(cumulative=True)\n",
    "cfd.tabulate(\n",
    "    conditions=['English', 'German_Deutsch'], \n",
    "    samples=range(1,10,1), \n",
    "    cumulative=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "           Friday    Monday  Saturday    Sunday  Thursday   Tuesday Wednesday \n   news        41        54        33        51        20        43        22 \nromance         3         2         4         5         1         3         3 \n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import brown\n",
    "\n",
    "days=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "cfd=nltk.ConditionalFreqDist(\n",
    "    (genre, word)\n",
    "    for genre in ['news', 'romance']\n",
    "    for word in brown.words(categories=genre)\n",
    "    if word in days\n",
    ")\n",
    "cfd.plot()\n",
    "cfd.tabulate(samples=sorted(days))"
   ]
  },
  {
   "source": [
    "### 2.2.4 使用二元语法（双边词）生成随机文本\n",
    "nltk.bigrams()生成连续的词对链表"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[('In', 'the'), ('the', 'beginning'), ('beginning', 'God'), ('God', 'created'), ('created', 'the'), ('the', 'heaven'), ('heaven', 'and'), ('and', 'the'), ('the', 'earth'), ('earth', '.for ')]\n"
     ]
    }
   ],
   "source": [
    "sent = ['In', 'the', 'beginning', 'God', 'created', 'the', 'heaven', 'and', 'the', 'earth', '.for ']\n",
    "print(list(nltk.bigrams(sent)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "bigrams=  <generator object bigrams at 0x0000000010BEFD58>\ncfd['living']=  <FreqDist with 6 samples and 16 outcomes>\ndict(cfd['living'])=  {'creature': 7, 'thing': 4, 'soul': 1, '.': 1, 'substance': 2, ',': 1}\ncfd['living'].max()=  creature\ncfd['finding'].max()=  him\n"
     ]
    }
   ],
   "source": [
    "text = nltk.corpus.genesis.words('english-kjv.txt')\n",
    "bigrams = nltk.bigrams(text)\n",
    "cfd = nltk.ConditionalFreqDist(bigrams)\n",
    "\n",
    "# 打印出来的结果 和 直接访问的结果不一样\n",
    "print(\"bigrams= \", bigrams)\n",
    "print(\"cfd['living']= \",cfd['living'])\n",
    "print(\"dict(cfd['living'])= \", dict(cfd['living']))\n",
    "print(\"cfd['living'].max()= \", cfd['living'].max())\n",
    "print(\"cfd['finding'].max()= \", cfd['finding'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--------------- >living< ---------------\nliving creature that he said , and the land of the land of the land \n--------------- >beginning< ---------------\nbeginning of the land of the land of the land of the land of the \n--------------- >finding< ---------------\nfinding him , and the land of the land of the land of the land \n"
     ]
    }
   ],
   "source": [
    "# P59 Ex2-1 产生随机文本\n",
    "def generate_model(cfdist, word, num=15):\n",
    "    for i in range(num):\n",
    "        print(word, end=' ')\n",
    "        word = cfdist[word].max()  # 选择与原单词匹配度最大的单词作为下一个单词\n",
    "    print()\n",
    "\n",
    "show_subtitle('living')\n",
    "generate_model(cfd, 'living')\n",
    "show_subtitle('beginning')\n",
    "generate_model(cfd, 'beginning')\n",
    "show_subtitle('finding')\n",
    "generate_model(cfd, 'finding')"
   ]
  },
  {
   "source": [
    "P59, 表2-4：NLTK 中的条件频率分布：定义、访问和可视化一个计数条件频率分布的常用方法和习惯用法"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}